Apache Spark has gained quick real-world adoption. It is one of the largest open source communities in Big Data over 200 contributors. In this article, Apache Spark Developers are sharing different use cases of this popular platform with you.
Streaming data
Apache Spark is known for its ability to process streaming data. Due to excessive data processed every day, it is now important for companies to gain potential for streaming and analyzing data in real time. Spark offers a feature of streaming to handle extra pressure during work.
Businesses of today are applying Spark Streaming in general ways, which are as under:
Streaming ETL:
Traditional ETL (Extract, Transform, and Load) tools applied by developers for batch processing in data warehouse environments must be able to read data, make data conversions in a compatible format, and write it to the target warehouse. Developers are prepared to clean data regularly with Streaming ETL. They can aggregate data previous to pushing it inside data stores.
Data enrichment:
Spark Streaming helps enrich live data by combining it with static data. It lets organizations perform a complete analysis of real-time data. Online advertisers leverage data enrichment feature to combine both historical customer data and live customer behavior data. It allows advertisers to deliver more personalized and targeted advertisements in real-time, and these ads are customer-driving.
Trigger event detection:
Using Spark Streaming, organizations are ready to detect and respond instantly to trigger events that could indicate serious issue inside the system. Triggers utilized in various organizations, including financial institutions and hospitals.
Machine learning:
Spark includes an integrated framework that performs advanced analytics to assist users in running repeated queries on data sets. MLlib is one of the components of Spark that can work in areas, such as clustering, dimensionality reduction, classification, etc. It empowers businesses to apply Spark for common big data functions, marketing purposes, and sentiment analysis.
Interactive Analysis:
Interactive analytics is the most notable capability feature of the Spark. Map Reduce was intended to manage batch processing; engines like Hive or Pig are slower for interactive analysis. A combination of spark and visualization tools helps interactively process and visualize complex data sets.
Apache Spark developers know that the framework will continue to develop its private ecosystem. Since big data has become the norm, the business management team has to find the best way to leverage it. Developers may expect more opportunities in the coming years to see the true power of Spark.
